A Multi-Objective Approach for Materialized View Selection

A Multi-Objective Approach for Materialized View Selection

Jay Prakash, T.V. Vijay Kumar
DOI: 10.4018/IJORIS.2019040101
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In today's world, business transactional data has become the critical part of all business-related decisions. For this purpose, complex analytical queries have been run on transactional data to get the relevant information, from therein, for decision making. These complex queries consume a lot of time to execute as data is spread across multiple disparate locations. Materializing views in the data warehouse can be used to speed up processing of these complex analytical queries. Materializing all possible views is infeasible due to storage space constraint and view maintenance cost. Hence, a subset of relevant views needs to be selected for materialization that reduces the response time of analytical queries. Optimal selection of subset of views is shown to be an NP-Complete problem. In this article, a non-Pareto based genetic algorithm, is proposed, that selects Top-K views for materialization from a multidimensional lattice. An experiments-based comparison of the proposed algorithm with the most fundamental view selection algorithm, HRUA, shows that the former performs comparatively better than the latter. Thus, materializing views selected by using the proposed algorithm would improve the query response time of analytical queries and thereby facilitate in decision making.
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1. Introduction

The penetration of smart technologies has made it increasingly convenient to capture and store the data of day to day business transactions. It has become standard practice in the business world to make every transaction in digital form. The transactional data has a hidden value, which can provide useful insight about business performance. These trends and insights aid smarter business decisions. If this transactional data is analyzed and used properly, it can empower the business world to make smarter decisions about their future business operations. In today’s competitive business environment, smarter decisions are necessary in order to sustain in the global market. The multi-national companies capture business transactional data and store them in multiple disparate databases spread across the globe.

There are two approaches to access this information namely the lazy (on-demand) approach or eager (in-advance) approach (Widom, 1995). In the former approach, the data is gathered based on the user query and is used when data at local data sources changes frequently. In the latter approach, the data is accumulated and stored apriori in a central repository and queries are processed against this already stored information. A data warehouse is based on the latter approach. In a data warehouse, relevant data accumulated from multiple disparate databases, spread across multiple locations, is integrated and stored for analytical query processing. A data warehouse stores subject-specific data, which is non-volatile and time-variant, integrated from multiple sources for supporting strategic decision making (Inmon, 2003; Kimball & Ross, 2003). The complex analytical queries are posed against the data in the data warehouse in order to get insights and trends for business operations. These complex and analytical queries take a lot of time for processing considering that a data warehouse grows continuously with time as data in it is non-volatile. This processing time can be reduced by materializing views in the data warehouse and use these for querying purposes (Mohania et al., 1999). Since all possible views cannot be materialized due to storage space constraints, an appropriate subset amongst them needs to be selected that conform to the storage space constraint and can result in efficient decision making. The selection of such a subset of relevant views is referred to as view selection (Chirkova et al., 2002). View selection is discussed next.

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